IMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE

The imbalance of datasets in chest X-ray presents a significant challenge in building accurate and reliable pre-diagnosis models. Imbalance occurs when one label within the dataset has a much lower occurrence compared to other labels. Utilizing an imbalanced dataset for pre-diagnosis model constr...

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Main Author:	Faris Muzakki, Muhammad
Format:	Theses
Language:	Indonesia
Online Access:	https://digilib.itb.ac.id/gdl/view/73938
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Institution:	Institut Teknologi Bandung
Language:	Indonesia

id	id-itb.:73938
spelling	id-itb.:739382023-06-25T09:51:43ZIMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE Faris Muzakki, Muhammad Indonesia Theses generative adversarial network, pneumonia infection, chest x-ray, classification INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/73938 The imbalance of datasets in chest X-ray presents a significant challenge in building accurate and reliable pre-diagnosis models. Imbalance occurs when one label within the dataset has a much lower occurrence compared to other labels. Utilizing an imbalanced dataset for pre-diagnosis model construction can lead to underfitting and overfitting conditions. Although some studies have been conducted by adapting learning algorithms, such approaches do not address the issue of imbalanced data distribution. In this paper, we generate synthetis X-ray images using generative adversarial network algorithms to enhance the classification model for pneumonia infection cases. This study produces synthesis X-ray images with lower Fréchet Inception Distance score compared to conventional data augmentation and SMOTE. Additionally, the classification model with the addition of synthesis data yields a significant improvement in F1 scores based on the Mann- Whitney U test. text
institution	Institut Teknologi Bandung
building	Institut Teknologi Bandung Library
continent	Asia
country	Indonesia Indonesia
content_provider	Institut Teknologi Bandung
collection	Digital ITB
language	Indonesia
description	The imbalance of datasets in chest X-ray presents a significant challenge in building accurate and reliable pre-diagnosis models. Imbalance occurs when one label within the dataset has a much lower occurrence compared to other labels. Utilizing an imbalanced dataset for pre-diagnosis model construction can lead to underfitting and overfitting conditions. Although some studies have been conducted by adapting learning algorithms, such approaches do not address the issue of imbalanced data distribution. In this paper, we generate synthetis X-ray images using generative adversarial network algorithms to enhance the classification model for pneumonia infection cases. This study produces synthesis X-ray images with lower Fréchet Inception Distance score compared to conventional data augmentation and SMOTE. Additionally, the classification model with the addition of synthesis data yields a significant improvement in F1 scores based on the Mann- Whitney U test.
format	Theses
author	Faris Muzakki, Muhammad
spellingShingle	Faris Muzakki, Muhammad IMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE
author_facet	Faris Muzakki, Muhammad
author_sort	Faris Muzakki, Muhammad
title	IMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE
title_short	IMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE
title_full	IMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE
title_fullStr	IMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE
title_full_unstemmed	IMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE
title_sort	image synthesis using generative adversarial network to overcome imbalance problems in chest x-ray image classification case
url	https://digilib.itb.ac.id/gdl/view/73938
_version_	1822993442180956160

IMAGE SYNTHESIS USING GENERATIVE ADVERSARIAL NETWORK TO OVERCOME IMBALANCE PROBLEMS IN CHEST X-RAY IMAGE CLASSIFICATION CASE

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